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      CNN_backbone(10)_HRNet
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<ul>
<li><a href="#%E9%A2%84%E5%A4%87%E7%9F%A5%E8%AF%86">预备知识</a></li>
<li><a href="#%E6%A6%82%E8%BF%B0">概述</a></li>
<li><a href="#%E7%9B%B8%E5%85%B3%E5%B7%A5%E4%BD%9C">相关工作</a></li>
<li><a href="#%E6%B7%B1%E5%85%A5%E7%90%86%E8%A7%A3">深入理解</a></li>
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<h2><span id="预备知识">预备知识</span></h2>
<ul>
<li>分辨率：比如说显示器的分辨率为$1920 \times 1080$。常见的分辨率：$1080p(1920 \times 1080), 720p(1280 \times 720), 4k(3840 \times 2160)$</li>
<li>高分辨率(High Definition)：简称HD，指垂直分辨率大于等于720的图像或视频，也称为高清图像或高清视频，上述列举的分辨率都属于高分辨率。</li>
<li>热图(Heatmap)：用暖色表示数值大，冷色表示数值小。</li>
<li>解剖学关键点：Anatomical keypoints(e.g., elbow, wrist, etc.)</li>
<li>Human pose estimation：人体姿态估计</li>
<li>2D human pose estimation的应用：人类行为识别(human action recognition)、人机交互(human-computer interaction)、动画(animation)等。</li>
</ul>
<h2><span id="概述">概述</span></h2>
<p><strong>标题</strong>：Deep High-Resolution Representation Learning  for Human Pose Estimation</p>
<p><strong>针对问题</strong>：Single-person pose estimation（单人姿态估计，这是多人姿态估计、视频姿态估计、跟踪的基础）</p>
<p><strong>背景</strong>：大多数现有工作都是通过输入高分辨率-降低分辨率-恢复高分辨率的方法来获得高分辨率的结果。</p>
<p><strong>主要工作</strong>：在整个处理过程中，一直维持图片的高分辨率表示，并反复融合多分辨率表示，呈现可靠的高分辨率表示。即：</p>
<ol>
<li>Maintain the high resolution through the whole process without the need of recovering the high resolution;</li>
<li>Fuse multi-resolution representations repeatedly, rendering reliable high-resolution representations.</li>
</ol>
<p><strong>具体实现</strong>：</p>
<ul>
<li>以高分辨率子网作为第一阶段，逐层加上从高分辨率转到低分辨率的子网，以此形成更多的阶段，而且以并联的方式连接这些子网(We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the multi-resolution subnetworks in parallel)。</li>
<li>并在并联的各个主线之间加上信息交流(We conduct repeated multi-scale fusions such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich high-resolution representations)。</li>
</ul>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/HRNet/1.png" alt></p>
<p>(Illustrating the architecture of the proposed HRNet. It consists of parallel high-to-low resolution subnetworks with repeated information exchange across multi-resolution subnetworks(multi-scale fusion). The horizontal and vertical directions correspond to the depth of the network and the scale of the feature maps, respectively.)</p>
<p><strong>实验结果</strong>：</p>
<ul>
<li>The predicted keypoint heatmap is potentially more accurate and spatially more precise.
<ul>
<li>将不同的分辨率并联，使得空间上的信息表述更加准确；</li>
<li>在同级进行信息交互（而不是将不同层次的信息进行交互，即不是将不同层次的feature-maps进行交互），使得预测的热图更加精确。</li>
</ul>
</li>
<li>Superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset.</li>
<li>Superior results in pose tracking on the PoseTrack dataset.</li>
</ul>
<h2><span id="相关工作">相关工作</span></h2>
<p><strong>关键点热图估计(Keypoint heatmap estimation)的主流工作</strong>：卷积神经网络方向(high-to-low and low-to-high framework)</p>
<ul>
<li>一个主的、类似于分类网络的子网络，用来降低输入的分辨率；</li>
<li>一个产生和其输入分辨率相同的输出的主体；</li>
<li>最后跟着一个回归器，用来估计关键位置在输出热图上的位置，然后转换为全分辨率；</li>
<li>中间可能还有多分辨率混合以及深度监督。</li>
</ul>
<p><strong>主流工作的一般模型</strong>：</p>
<ol>
<li>对称的high-to-low和low-to-high处理流程；</li>
<li>Heavy high-to-low and light low-to-high;</li>
<li>Combination with dilated convolutions.</li>
</ol>
<h2><span id="深入理解">深入理解</span></h2>
<p>现有工作都是将其串联，如下图所示：</p>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/HRNet/2.png" alt="图片来自https://zhuanlan.zhihu.com/p/143385915"></p>
<p>而HRNet则是将不同分辨率的feature-maps进行并联，如下图所示：</p>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/HRNet/3.png" alt></p>
<p>然后在并联的基础上，添加不同分辨率feature-maps之间的信息交互（fusion），注意为了便于实现信息交互，中间引入了一个叫做<strong>exchange units</strong>的东西：</p>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/HRNet/1.png" alt></p>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/HRNet/4.png" alt></p>
<p>Fusion的具体方法，如下图所示：</p>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/HRNet/5.png" alt></p>
<p>（一个想法：能不能直接把左边3个feature-maps整合到一起，然后通过某种随机策略或者其他策略，pooling或者卷积，得到右边3个feature-maps？）</p>

      
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